Data loading method, transmitter, and base station

ABSTRACT

The invention is related to a data loading method in a communication system where sub-carriers include eigenmodes, comprising, for instance: arranging eigenmodes into a predetermined number of clusters, each cluster comprising eigenmodes of different quality levels; pre-allocating transmission power to the eigenmodes with the aid of the calculated required power for achieving the approximated signal-to-noise ratio; determining collective transmission power to be allocated to each cluster based on the pre-allocation; allocating collective transmission power to the eigenmodes.

BACKGROUND

Systems based on using multiple antennas are one of the most promisingtechniques for achieving high data rates. By combining MIMO (multipleinput multiple output) techniques with OFDM (orthogonal frequencydivision multiplexing) modulation, the frequency selective MIMO channelis turned into a set of frequency flat MIMO fading channels which can beindividually processed.

If a channel is known at a transmitter side, the channel matrix can bedecomposed for each sub-carrier using SVD (singular valuedecomposition). As a result a set of orthogonal sub-channels is obtainedin the space domain. These elementary sub-channels are also calledeigenmodes.

One prior art method to find the optimal bit and power allocation for aset of multiple parallel sub-channels (for example, for eigenmodes of aMIMO-OFDM system) is Hughes-Hartog (HH) algorithm. Hughes-Hartogalgorithm is depicted in John A. C: Bingham: Multicarrier Modulation forData Transmission: An Idea Whose Time Has Come, IEEE CommunicationsMagazine, pp. 5-14, May 1990, which is taken herein as a reference.However, the required computational effort of Hughes-Hartog algorithmincreases with the average bit rate and therefore it is not a practicalsolution for high bit rate systems Some sub-optimal fast loadingalgorithms have been proposed in Peter S. Chow, John M. Cioffi and JohnA. C. Bingham: A Practical Discrete Multitone Transceiver LoadingAlgorithm over Spectral Shaped Channels, IEEE Trans. on Communications,vol. 43, no. 2/3/4, pp. 773-775, February/March/April 1995, Robert F. H.Fischer and Johannes B. Huber: A New Loading Algorithm for DiscreteMultitone Transmission, IEEE Proceeding of Global TelecommunicationsConferences, vol. 1, pp. 724-728, November 1996 and Yu Wei and John M.Cioffi: On Constant Power Water-Filling”, IEEE Proceeding ofInternational Conference on Communications, vol. 6, pp. 1665-1669, June2001 which are taken herein as a reference.

These prior art methods are developed for slow varying channels likeasymmetric digital subscriber line. For such channels, the signallingoverhead required, for instance, for informing a receiver about themodulation scheme of each eigenmode can be neglected due to possibilityto update the transmission parameters at relatively long time intervals.However, when the channel is changing fast, as is the case in wirelesssystems, the transmission parameters can be kept constant only for atime period shorter than the channel coherence time and therefore theamount of signalling overhead is significant.

BRIEF DESCRIPTION OF THE INVENTION

According to an aspect of the invention, there is provided a dataloading method in a communication system where sub-carriers includeeigenmodes, comprising: estimating a channel matrix; calculating asingular value decomposition of the estimated channel matrix forobtaining eigenvalue estimates; defining biases between eigenvalues andeigenvalue estimates and performing a channel estimation reliabilitytest based on the defined biases; carrying out bias compensation foreigenvalue estimates based on the defined biases; calculating equivalentpower gain; arranging eigenmodes into a predetermined number ofclusters, each cluster comprising eigenmodes of different qualitylevels; pre-allocating transmission power to the eigenmodes according totheir capacity by using the calculated equivalent power gain;determining collective transmission power to be allocated to eachcluster based on the preallocation, selecting the optimum modulation andcoding scheme and allocating collective transmission power to theeigenmodes.

According to another aspect of the invention, there is provided atransmitter of a communication system where sub-carriers are dividedinto eigenmodes, comprising: means for estimating a channel matrix;means for calculating a singular value decomposition of the estimatedchannel matrix for obtaining eigenvalue estimates; means for definingbiases between eigenvalues and eigenvalue estimates and performing achannel estimation reliability test based on the defined biases; meansfor carrying out bias compensation for eigenvalue estimates based on thedefined biases; means for calculating equivalent power gain; means forarranging eigenmodes into a predetermined number of clusters, eachcluster comprising eigenmodes of different quality levels; means forpre-allocating transmission power to the eigenmodes according to theircapacity by using the calculated equivalent power gain; means fordetermining collective transmission power to be allocated to eachcluster based on the pre-allocation; means for selecting the optimummodulation and coding scheme and allocating collective transmissionpower to the eigenmodes.

According to another aspect of the invention, there is provided atransmitter of a communication system where sub-carriers are dividedinto eigenmodes, configured to: estimate a channel matrix; calculate asingular value decomposition of the estimated channel matrix forobtaining eigenvalue estimates; define biases between eigenvalues andeigenvalue estimates and performing a channel estimation reliabilitytest based on the defined biases; carry out bias compensation foreigenvalue estimates based on the defined biases; calculate equivalentpower gain; arrange eigenmodes into a predetermined number of clusters,each cluster comprising eigenmodes of different quality levels;pre-allocate transmission power to the eigenmodes according to theircapacity by using the calculated equivalent power gain; determinecollective transmission power to be allocated to each cluster based onthe pre-allocation; select the optimum modulation and coding scheme andallocating collective transmission power to the eigenmodes.

According to another aspect of the invention, there is provided a basestation of a communication system where sub-carriers are divided intoeigenmodes, comprising: means for estimating a channel matrix; means forcalculating a singular value decomposition of the estimated channelmatrix for obtaining eigenvalue estimates; means for defining biasesbetween eigenvalues and eigenvalue estimates and performing a channelestimation reliability test based on the defined biases; means forcarrying out bias compensation for eigenvalue estimates based on thedefined biases; means for calculating equivalent power gain; means forarranging eigenmodes into a predetermined number of clusters, eachcluster comprising eigenmodes of different quality levels; means forpre-allocating transmission power to the eigenmodes according to theircapacity by using the calculated equivalent power gain; means fordetermining collective transmission power to be allocated to eachcluster based on the pre-allocation; means for selecting the optimummodulation and coding scheme and allocating collective transmissionpower to the eigenmodes.

According to another aspect of the invention, there is provided a basestation of a communication system, where sub-carriers are divided intoeigenmodes, configured to: estimate a channel matrix; calculate asingular value decomposition of the estimated channel matrix forobtaining eigenvalue estimates; define biases between eigenvalues andeigenvalue estimates and performing a channel estimation reliabilitytest based on the defined biases; carry out bias compensation foreigenvalue estimates based on the defined biases; calculate equivalentpower gain; arrange eigenmodes into a predetermined number of clusters,each cluster comprising eigenmodes of different quality levels;pre-allocate transmission power to the eigenmodes according to theircapacity by using the calculated equivalent power gain; determinecollective transmission power to be allocated to each cluster based onthe preallocation; select the optimum modulation and coding scheme andallocating collective transmission power to the eigenmodes.

Embodiments of the invention are described in the dependent claims.

The method and system of the invention provide several advantages. In apreferred embodiment of the invention, a sub-optimal low-complexity bitand power loading method that requires low signalling overhead isprovided. Therefore the algorithm is especially suitable for high bitrate systems. The algorithm is also robust against channel estimationerrors at the transmitter side.

LIST OF DRAWINGS

In the following, the invention will be described in greater detail withreference to the preferred embodiments and the accompanying drawings, inwhich

FIG. 1 shows an example of a communication system,

FIG. 2 is a flow chart, and

FIG. 3 illustrates an example of a transmitter structure.

DESCRIPTION OF EMBODIMENTS

With reference to FIG. 1, we examine an example of a data transmissionsystem in which the preferred embodiments of the invention can beapplied. The present invention can be applied in various wirelesscommunication systems based on MIMO-OFDM. One example of such acommunication system is IEEE 802.11a wireless LAN communication system.The basic idea of OFDM is to split a high-rate data stream into parallelstreams that are transmitted simultaneously over different orthogonalsub-carriers. An OFDM signal consists of a sum of sub-carriers that aremodulated by using phase shift keying (PSK) or quadrature amplitudemodulation (QAM). MIMO systems include multiple transmission andreception antennas. It is clear to a person skilled in the art that themethod according to the invention can be applied to systems utilizingdifferent modulation methods or air interface standards.

In MIMO-OFDM systems, the frequency selective MIMO channel is turnedinto a set of frequency flat MIMO channels which can be individuallyprocessed. This reduces computational complexity. Elementary orthogonalsub-channels of each sub-carrier, obtained by using SVD (singular valuedecomposition) of a channel matrix at each sub-carrier, can also becalled eigenmodes.

The embodiments are not, however, restricted to the system given as anexample but a person skilled in the art may apply the solution in othersystems provided with the necessary properties.

FIG. 1 is a simplified illustration of a digital data transmissionsystem to which the solution according to the invention is applicable.This is a part of a cellular radio system, which comprises a basestation or an equivalent network element 100, which has bi-directionalradio links 102 and 104 to subscriber terminals 106 and 108. Thesubscriber terminals may be fixed, vehicle-mounted or portable. The basestation includes transmitters, for instance. From the transceivers ofthe base station there is a connection to an antenna unit, whichestablishes the bi-directional radio links to the subscriber terminal.The base station is further connected to a base station controller or anequivalent network element 110, which transmits the connections of theterminals to the other parts of the network. The base station controllercontrols in a centralized manner several base stations connected to it.

The cellular radio system can also communicate with other networks suchas a public switched telephone network or the Internet.

If a transmitter has some knowledge about CSI (channel stateinformation) the spectral efficiency can be increased by applyingadaptive transmission techniques. In a TDD (time division duplex) basedsystem, the channel can be estimated at the base station on the basis ofa signal received in one or more selected previous uplink timeslots. Theestimate may be used for link adaptation during the next down linktimeslot. The link adaptation consists of modifying transmissionparameters according to channel variation in order to maximise thethroughput using at most the maximum transmission power and fulfillingrequirements set for the reliability of the transmission. Typically, thereliability is evaluated in terms of frame error rate (FER) or bit errorrate (BER).

The transmitter typically informs the receiver about the chosentransmission parameters, for instance selected eigenmodes,constellations, powers allocated to the eigenmodes and channel codeparameters, using a signalling channel. The signalling overhead reduceseffective capacity of the downlink channel, thus the target is tominimize it for making the usage of available capacity more efficient.Next, by the aid of FIG. 2, let us examine in further detail anembodiment of the data loading method (or algorithm). The example systemused here includes two receiver antennas and at least two transmittingantennas.

The scheme exploits radio channel reciprocity of TDD (time divisionduplex) transmission and operates in an open loop adaptation mode.Typically, for each transmitted frame, in order to maintain the selectedframe error rate (FER) under the total transmitted power constraint, themodulation scheme and the allocated power to each eigenmode are adaptedaccording to channel conditions.

If a channel is known at a transmitter side, the channel matrix can bedecomposed for each sub-carrier using SVD (singular valuedecomposition). As a result a set of orthogonal sub-channels is obtainedin the space domain. These elementary sub-channels are also called inthis application eigenmodes.

For such a scenario, the optimal signalling in terms of channel capacityis usually eigenmode transmission, where a complex symbol is allocatedto each eigenmode and the transmission power is allocated to theeigenmodes according to, for instance, a prior art water fillingalgorithm or modified water filling algorithm. The modified waterfilling algorithm is explained later in further detail. The process ofadapting power and the amount of information to be allocated to eacheigenmode is called here bit and power loading. The amount ofinformation to be allocated to the eigenmodes varies according to themodulation level such as complex signal constellation and/or the channelcoding parameters such as code rate. MIMO-OFDM eigenmode transmission isdescribed in Kai-Kit Wong, Roger S. K. Cheng, Khaled Ben Letaief andRoss D. Murch: Adaptive Spatial-Subcarrier Trellis Coded MQAM and PowerOptimization for OFDM transmissions, Proceeding of IEEE VehicularTechnology Conference, vol. 3, pp. 2049-2053, 2000, H. Sampath, P.Stoica and Arogyaswami J. Pauiraj: Generalized linear precoder anddecoder design for MIMO channels using the weighted MMSE criterion, IEEETrans on Communications, vol. 49, no. 12, pp. 2198-2206, December 2001.

The embodiment of the method provides reduced signalling overhead inrelation to the number of sub-carriers by grouping eigenmodes intopreferably two clusters and using the same modulation and coding scheme(MCS) for all selected eigenmodes belonging to the same cluster. Theclusters preferably include the strongest and the weakest eigenmodes ofeach subcarrier.

The embodiment starts in block 200.

In block 202, a channel matrix is estimated.

An estimate of the channel matrix (Ĥ_(c)) at each sub-carrier c isprovided:Ĥ _(c) =H _(c) +N _(c)  (1)

-   -   wherein    -   c means a sub-carrier and    -   Ĥ_(c) means the estimated channel matrix and    -   H_(c) means the true value of channel matrix and    -   N_(c) means the means channel estimation error with the entries        modelled as independent Gaussian random variables with zero mean        and variance σ_(N) ².

Channel estimation is typically made according to selected prior artmethod. The channel estimation is usually based on pilot signals of thereversed frame. Variance of the error of the channel estimation (σ_(N)²) is typically also estimated using an appropriate prior art method.Channel estimation is well known in prior art and therefore it is notexplained here in further detail.

In block 204, singular value decomposition (SVD) of the estimatedchannel matrix is calculated. As a result the eigenvalues of theestimated channel matrix are obtained. Singular value decomposition iscalculated typically independently for each sub-carrier.

The SVD of the estimated channel matrix is given by:Ĥ _(c) =Û _(c){circumflex over (Λ)}_(c) V _(c) ^(H),  (2)

-   -   wherein    -   c means sub-carrier and    -   Û_(c) is an RXR (R=the number of receiver antennas equal with 2        in this example) unitary matrix contains in its columns left        singular vectors of the channel matrix and    -   {circumflex over (V)}_(c) is an TXT (T=the number of transmitter        antennas≧2 in this example) unitary matrix contains in its        columns right singular vectors of the channel matrix and    -   {circumflex over (V)}_(c) ^(H) means the transpose and complex        conjugate (hermitian) of the matrix {circumflex over (V)}_(c)        and    -   {circumflex over (Λ)}_(c) is an RXT (R=the number of receiver        antennas equal with 2 in this example, T=the number of        transmitter antennas) dimensional matrix having the elements        {square root}{square root over ({circumflex over (λ)})}_(1,c),        {square root}{square root over ({circumflex over (λ)})}_(2,c) on        the main diagonal and all other elements being zeros; the        elements on the main diagonal are descending ordered {square        root}{square root over ({circumflex over (λ)})}_(1,c), {square        root}{square root over ({circumflex over (λ)})}_(2,c).

The matrix {circumflex over (V)}_(c) is also used for linearpre-filtering of the signal transmitted at a sub-carrier c in order toobtain eigemodes. Due to the channel estimation errors orthogonalitybetween the resulted eigenmodes in each subcarrier is usually lost.Therefore a spatial equalizer is required at the receiver side for eachsub-carrier. The spatial equalizer can be implemented either in zeroforcing or linear minimum mean square structure. The equalization iswell known in prior art and therefore it is not explained here infurther detail.

If the channel estimation is fairly accurate (channel estimation errorssmaller than 10%), the instantaneous signal to noise ratio at theequalizer output can be approximated by: $\begin{matrix}{{{SNR}_{i,c} \approx {\frac{P_{i,c}}{N_{0}}\lambda_{i,c}{{{\hat{v}}_{i,c}^{H}v_{i,c}}}^{2}}},} & (3)\end{matrix}$

-   -   wherein    -   c means sub-carrier and    -   i means the spatial sub-channel (eigenmode) index for a given        subcarrier and    -   N₀ means the power spectral density of noise at the receiver        side and    -   P_(i,c) means the power allocated at the eigenmode (i, c) and    -   λ_(i,c) means the square of the i'th singular value of the true        channel matrix H_(c). {circumflex over (v)}_(i,c) is the i'th        right singular vector of the estimated channel matrix Ĥ_(c) and        is given by the i'th column of matrix {circumflex over (V)}_(c)        and    -   {circumflex over (v)}_(i,c) ^(H) means the transpose and complex        conjugate (hermitian) of the vector {circumflex over (v)}_(i,c)        and    -   v_(i,c) is the i'th right singular vector of the true channel        matrix H_(c).

The term |{circumflex over (v)}_(i,c) ^(H)v_(i,c)|² in equation (3)represents the loss in SNR due to the channel estimation errors. It'sinstantaneous values are difficult to calculate but it was found bysimulations that: $\begin{matrix}{{E_{N_{c}}\left\{ {{{{\hat{v}}_{i,c}^{H}v_{i,c}}}^{2}\frac{{\hat{\lambda}}_{i,c}}{\lambda_{i,c}}} \right\}} \approx 1} & (4)\end{matrix}$

-   -   wherein    -   c means sub-carrier and    -   i means the spatial sub-channel (eigenmode) index for a given        subcarrier and    -   E_(N) _(c) { } means the statistical expectation operator, where        the expectation is taken with respect of probability density        function of N_(c) elements (N_(c) is the channel estimation        error) and    -   λ_(i,c) means the square of the i'th singular value of the true        channel matrix H_(c) and    -   {circumflex over (λ)}_(i,c) means the square of the i'th        singular value of the estimated channel matrix Ĥ_(c).

Based on relations (3) and (4), the average SNR (signal-to-noise ratio)loss can be compensated by increasing the instantaneous allocated powerto each eigenmode with the ratio$\frac{{\hat{\lambda}}_{i,c}}{\lambda_{i,c}}.$To implement this we need a reliable estimate of the λ_(i,c).Eigenvalues are squares of the singular values of the channel matrixobtained by SVD.

In practise, the channel matrix itself cannot determine. Instead of thereal channel matrix, the estimate is used. Correspondingly, also foreigenvalues estimates are used.

The main idea of the method is to partially compensate the bias between{circumflex over (λ)}_(i,c) and λ_(i,c). Notice that λ_(i,c) are theeigenvalues of the hermitian matrix H_(c)H_(c) ^(H) and {circumflex over(λ)}_(i,c) are the eigenvalues of the hermitian matrix Ĥ_(c)Ĥ_(c) ^(H).

Let's denote the entries in the 2×2 hermitian matrix H_(c)H_(c) ^(H) asfollows: ${H_{c}H_{c}^{H}} = \begin{bmatrix}a_{c} & b_{c} \\b_{c}^{*} & c_{c}\end{bmatrix}$

-   -   wherein    -   a_(c),b_(c),b_(c),c_(c) represent matrix elements and    -   * means a complex conjugate.

By solving the characteristic equation of the matrix H_(c)H_(c) ^(H) theeigenvalues are given by: $\begin{matrix}\begin{matrix}{\lambda_{1,c} = {\frac{1}{2}\left( {a_{c} + c_{c} + \sqrt{\left( {a_{c} - c_{c}} \right)^{2} + {4{b_{c}}^{2}}}} \right)}} \\{\lambda_{2,c} = {\frac{1}{2}\left( {a_{c} + c_{c} - \sqrt{\left( {a_{c} - c_{c}} \right)^{2} + {4{b_{c}}^{2}}}} \right)}}\end{matrix} & (6)\end{matrix}$

-   -   wherein    -   a_(c),b_(c),c_(c) are defined in (5) and    -   | | means absolute value.

The estimated channel matrix Ĥ_(c) multiplied with its' transposecomplex-conjugate (Hermitian) Ĥ_(c) ^(H) matrix is expressed as follows:$\begin{matrix}{{{\hat{H}}_{c}{\hat{H}}_{c}^{H}} = \begin{bmatrix}{\hat{a}}_{c} & {\hat{b}}_{c} \\{\hat{b}}_{c}^{*} & {\hat{c}}_{c}\end{bmatrix}} & (7)\end{matrix}$

-   -   wherein    -   â_(c), {circumflex over (b)}_(c), {circumflex over (b)}_(c)*,        ĉ_(c) represent matrix elements and    -   * means a complex conjugate.

The eigenvalues of Ĥ_(c)Ĥ_(c) ^(H) are given by: $\begin{matrix}\begin{matrix}{{\hat{\lambda}}_{1,c} = {\frac{1}{2}\left( {{\hat{a}}_{c} + {\hat{c}}_{c} + \sqrt{\left( {{\hat{a}}_{c} - {\hat{c}}_{c}} \right)^{2} + {4{{\hat{b}}_{c}}^{2}}}} \right)}} \\{{\hat{\lambda}}_{2,c} = {\frac{1}{2}\left( {{\hat{a}}_{c} + {\hat{c}}_{c} - \sqrt{\left( {{\hat{a}}_{c} - {\hat{c}}_{c}} \right)^{2} + {4{{\hat{b}}_{c}}^{2}}}} \right)}}\end{matrix} & (8)\end{matrix}$

-   -   wherein    -   â_(c),{circumflex over (b)}_(c),ĉ_(c) are defined in (7) and    -   | | means absolute value.

In block 206, biases between eigenvalues λ_(i,c) and their estimates{circumflex over (λ)}_(i,c) are defined for channel estimationreliability test and the channel estimation reliability test isperformed. The biases between eigenvalues λ_(i,c) and their estimatesλ_(i,c), the terms of expressions (6) and (8), can be calculated by:B ₁ =E _(N) _(c) {â _(c) −a _(c) }=Tσ _(N) ²B ₂ =E _(N) _(c) {ĉ _(c) −c _(c) }=Tσ _(N) ²B ₃ E _(N) _(c) {|{circumflex over (b)} _(c)|² −|b _(c)|²}=σ_(N) ² ∥H_(c)∥_(F) ² +Tσ _(N) ⁴B ₄ =E _(N) _(c) {(â _(c) −ĉ _(c))²−(a _(c) −c _(c))²}=4σ_(N) ⁴ ∥H_(c)∥_(F) ²+2Tσ _(N) ²  (9)

-   -   wherein    -   B₁, B₂, B₃, B₄ are the bias terms and    -   a_(c),b_(c),c_(c) are defined in (5) and    -   â_(c),{circumflex over (b)}_(c),ĉ_(c) are defined in (7) and    -   E_(N) _(c) { } means the statistical expectation operator, where        the expectation is taken with respect of probability density        function of N_(c) (N_(c) is the channel estimation error) and    -   T is the number of transmission antennas and    -   σ_(N) ₂ is variance of the error of the channel estimation and    -   ∥H_(c)∥_(F) ² means the square of the Frobenius norm of channel        matrix H_(c) and it is given by ∥H_(c)∥_(F) ²=a_(c)+c_(c).

From (9) is clear that for computing bias terms B₃ and B₄ we need firstto estimate the square of the Frobenius norm of the channel matrixH_(c). An instantaneous consistent estimate is given by:{overscore (∥H _(c)∥_(F) ²)}=â _(c) +ĉ _(c) −B ₁ −B ₂ =â _(c) +ĉ_(c)−2Tσ _(N) ²  (10)

Based on this estimate a test for the channel estimation reliabilityevaluation is carried out for each sub-carrier as follows:if ({overscore (∥H_(c)∥_(F) ²)}×>0)  (11)

-   -   the sub-carrier c will be used in the loading algorithm else    -   the sub-carrier c will be discarded from the loading algorithm.

In block 208, bias compensation is carried out for eigenvalue estimates{circumflex over (λ)}_(i,c).

For the eigenvalue estimation the bias terms given by (9) are used topartially compensate the bias in the terms of expression (8) as follows:$\begin{matrix}\begin{matrix}{{\hat{\lambda}}_{1,c} = {\frac{1}{2}\left( {\left( {{\hat{a}}_{c} - B_{1}} \right)^{+} + \left( {{\hat{c}}_{c} - B_{2}} \right)^{+} +} \right.}} \\\left. \quad\sqrt{\left\lbrack {\left( {{\hat{a}}_{c} - {\hat{c}}_{c}} \right)^{2} - {\overset{\_}{B}}_{3}} \right\rbrack^{+} + {4\left\lbrack {{{\hat{b}}_{c}}^{2} - {\overset{\_}{B}}_{4}} \right\rbrack}^{+}} \right)\end{matrix} & (12) \\{{\hat{\lambda}}_{2,c} = {\frac{1}{2}\left( {\left( {{\hat{a}}_{c} - B_{1}} \right)^{+} + \left( {{\hat{c}}_{c} - B_{2}} \right)^{+} - \sqrt{\left( {{\hat{a}}_{c} - {\hat{c}}_{c}} \right)^{2} + {4{{\hat{b}}_{c}}^{2}}}} \right)}} & \quad\end{matrix}$

-   -   wherein    -   B₁,B₂ are the bias terms given by (9) and    -   (x)⁺ means the positive value of x defined as: (x)⁺=max (x, 0)        and    -   {overscore (B₃)} and {overscore (B₄)} represents the estimates        of B₃ and B₄; they are obtained by replacing ∥H_(c)∥_(F) ²        in (9) with its instantaneous estimate {overscore (∥H_(c)∥_(F)        ²)} given by (10).

For smaller eigenvalues ({tilde over (λ)}_(2,c) in equation (12)), theterms, which are under the square root are left uncompensated. Thisproduces a slight underestimation of the eigenvalue causing increasingin system robustness. The eigenmodes corresponding to the eigenvalueswhich become negative after bias compensation, are discarded from theloading algorithm.

In block 210, the equivalent power gain is calculated.

By using the estimated eigenvalues (12) in (4) and combining with (3) wecan express the equivalent power gain at the eigenmode (i,c) as follows:$\begin{matrix}{g_{i,c} = \frac{{\overset{\sim}{\lambda}}_{i,c}^{2}}{{\hat{\lambda}}_{i,c}}} & (13)\end{matrix}$

Clearly, in case of perfect channel estimation at the transmitter sidethe equivalent power gain become the square of the singular value of thechannel matrix$\left( {g_{i,c}\quad\underset{\sigma_{N}^{2}\rightarrow 0}{\longrightarrow}\quad\lambda_{i,c}} \right)$as is known from the prior art.

An eigenmode is an elementary sub-channel created within one sub-carrierby using SVD of the channel matrix at that sub-carrier. The eigenmodesare obtained by pre-filtering the transmitted signal with the right handsingular vector matrix.

The power required at the eigenmode (i,c) in order to obtain a giventarget signal to noise ratio, SNR_(i), is given by: $\begin{matrix}{{{\overset{\sim}{P}}_{i,c}\left( {SNR}_{t} \right)} = {{SNR}_{t}\frac{N_{0}}{g_{i,c}}}} & (14)\end{matrix}$

In the following, we will describe the bit and power loading part of thedata loading method, using as an example a system with two receivingantennas and at least two transmitter antennas. The number of eigenmodesobtained in each sub-carrier is given by the minimum between the numberof transmit and receive antennas (two in the considered example).

Briefly, the main idea of the method is to exploit the intrinsic spatialdiversity achieved by using MIMO-OFDM transmission. In the following wepresent one embodiment based on turbo channel coding but it is clear toa person skilled in the art that turbo codes may be replaced by otherprior art channel coding methods.

In block 212, eigenmodes are arranged into a predetermined number ofclusters, each cluster comprising eigenmodes of different qualitylevels.

In one embodiment, 2C eigenmodes (C meaning the total number ofsub-carriers) are grouped into two clusters, consisting of the strongestand the weakest eigenmodes from each sub-carrier, respectively. Exceptsome eigenmodes corresponding to the most faded sub-carriers, a smalldifference between the eigenmodes gains belonging to the same cluster isexperienced due to spatial diversity. Consequently, by skipping some ofthe most faded eigenmodes, the same MCS (modulation and coding scheme)can be used for all selected eigenmodes belonging to the same clusterand the required signalling overhead is C times reduced.

In one embodiment, each cluster is independently encoded by using singleinput single output (SISO) turbo code. The resulted bits are usuallyalso interleaved and modulated. For each cluster the encoding isperformed jointly in time and frequency domain, a codeword covering theselected eigenmodes from one cluster during whole transmitted frame. Inthis way the codeword is enlarged to achieve interleaving gain while theadaptability between clusters is still preserved.

The modulation and coding parameters (for example the complexconstellation used in modulation, the coding rate) are modifiedaccording to channel conditions in order to maintain the target FER(frame error rate). Specifically, there are available a set of Kdistinct modulation and coding schemes (MCS), and we denote them byb_(k), k=1, . . . ,K, the spectral efficiency being provided by k'thMCS.

For each available MCS, the required SNR to achieve the target FER inadditive white Gaussian noise (AWGN) channel can be obtainedanalytically or by preliminary simulations and the results can be storedinto a look up table.

Let's denote by γ_(k), k=1, . . . ,K the SNR required by the k'th MCS toachieve the target FER in AWGN channel. We assume that b_(k) are givenin the ascending order b₁<b₂< . . . <b_(K) and for an efficient MCS setis required that γ₁<γ₂< . . . <γ_(K).

For each transmitted frame, first, the total power is divided betweenthe clusters according with their capacities, and then, the MCS and theselected eigenmodes in each cluster are independently optimized tomaximize the throughput subject to equal SNR constraint for all selectedeigenmodes.

Thus, in block 214, transmission power is pre-allocated to theeigenmodes according to their capacity by using the calculatedequivalent power gain.

In practise, the amount of information allocated to an eigenmode islimited not only by the noise power but also by the maximum rateachieved by the most spectral efficient MCS (modulation and codingscheme). Therefore, in power allocation, instead of prior art waterfilling a modified water filling method can be used for taking intoaccount the saturation of signal-to-noise ratio of the eigenmode and theaverage signal-to-noise ratio gap.

In the modified water filling, the power to be pre-allocated for aneigenmode is expressed: $\begin{matrix}{{P_{i,c}^{({MWF})} = {\min\left\lbrack {\left( {\mu - {\frac{N_{0}}{g_{i,c}}G}} \right)^{+},{\frac{N_{0}}{g_{i,c}}\gamma_{K}}} \right\rbrack}},} & (15)\end{matrix}$

-   -   wherein    -   MWF means modified water filling    -   i is a cluster index,    -   c is a carrier index,    -   N₀ means spectral density of noise at the receiver,    -   G is the average signal-to-noise ratio gap between the Shannon        capacity and the spectral efficiency provided by the MCS set,    -   (x)⁺ means the positive value of x defined as: (x)⁺=max (x, 0)        and    -   γ_(K) means the SNR required by the most spectral efficient MCS        to achieve the target FER in AWGN (Additive White Gaussian        Noise) channel    -   g_(i,c) means the equivalent power gain at the eigenmode (i,c)        given by (13)    -   μ means the “water level” and it is found from the maximum        transmit power constraint: $\begin{matrix}        {{\sum\limits_{i = 1}^{2}{\sum\limits_{c = 1}^{C}\quad P_{i,c}^{({MWF})}}} = P_{T}} & (16)        \end{matrix}$    -   wherein    -   P_(T) means the maximum transmit power    -   C means the total number of sub-carriers.

When the modified water filling method described above is used, lesspower is allocated to some eigenmodes than if the prior art waterfilling method was used. These eigenmodes are called saturatedeigenmodes because although the amount of allocated power can beincreased, the amount of information transmitted using these eigenmodeshas already reached the maximum e.g. is saturated.

In block 216, based on the power pre-allocation of block 214, collectivepower to be allocated to each cluster is determined. The collectivepower allocated to a cluster can be expressed as follows:$\begin{matrix}{{P_{i} = {\sum\limits_{c = 1}^{C}\quad P_{i,c}^{({MWF})}}},} & (17)\end{matrix}$

-   -   wherein    -   C means the number of sub-carriers,    -   c is the current sub-carrier,    -   Σ means a summing operation and P_(i,c) ^((MWF)) is obtained        from equation (15).

In block 218, transmission power is allocated to the eigenmodes and theoptimum modulation and coding scheme (MCS) is selected for each cluster.

The clusters are independently optimised by searching for the MCS thatprovides maximum throughput when the collective power P_(i) is allocatedto the eigenmodes according to the target FER (frame error rate), underequal signal-to-noise constraint. This is, in practise, typicallyequivalent with the maximisation over the MCS set of the product betweenthe maximum number of eigenmodes selectable using a certain MCS and thespectral efficiency provided by that MCS.

The maximisation is preferably applied in the same manner to eachcluster. Therefore the cluster index (I) is omitted for brevity. Bydenoting with P the collective power allocated to the cluster, the indexof the optimum MCS, k₀, which maximises the throughput under the totaltransmitted power and the maximum FER constraints can be written as:$\begin{matrix}{{k_{0} = {{\underset{{k = 1},2,\quad\ldots\quad,K}{{\arg\quad\max}\quad}{b_{k}\left( {\max\limits_{S \Subset {{{\{{1,2,\quad\ldots\quad,C}\}}\text{:}{\sum\limits_{c \in S}{{\overset{\sim}{P}}_{c}{(\gamma_{k})}}}} \leq P}}{S}} \right)}} = {\arg\quad\underset{k}{\quad\max}\quad b_{k}s_{k}}}},} & (18)\end{matrix}$

-   -   wherein    -   S is a subset of the set of eigenmode indexes with cardinality        |S|,    -   s_(k) represents the maximum number of eigenmodes that can be        selected using the k'th modulation scheme,        ${{\overset{\sim}{P}}_{c}\left( \gamma_{k} \right)}\quad{is}\quad{given}\quad{by}\quad(14)\quad\left( {\gamma_{k}\frac{N_{0}}{g_{c}}} \right)$    -   C means the number of carriers,    -   c is the current carrier,    -   b_(k) means the spectral efficiency (number of bits per        transmitted complex symbol) provided by k'th MCS.

The values s_(k) can be calculated using the following algorithm:

Step1: arranging in a descending order the equivalent eigenmode powergains g_(c). The arranging process means mathematically that the indexesvectorj=[j₁,j₂, . . . ,j_(C)],  (19)

-   -   such that g_(j1)>g_(j2)> . . . >g_(jC) is found.

Step2: computing the vector w=[w₁,w₂, . . . ,w_(C)], with each entrygiven by${w_{c} = {N_{0}{\sum\limits_{n = 1}^{c}\quad\frac{1}{g_{j_{n}}}}}},$(20)

-   -   wherein    -   N₀ means the power spectral density of noise at the receiver        side and    -   c is the index of the entry and    -   n means the summation index,    -   j_(n) means the n'th entry in the indexes vector j given by (19)    -   g_(j) _(n) means the equivalent power gain of the j_(n)'th        eigenmode in the current cluster    -   Σ means the summing operation.

Step 3: Let s₀=C and γ₀=0. For k=1 to K, let s_(k)=s_(k−1) and if w_(s)_(k) γ_(k)>P decrease the number of selected eigenmodes, s_(k)=s_(k−1),until the power constraint, w_(s) _(k) γ_(k)<P, is satisfied. Every timewhen the power constraint is satisfied the corresponding value s_(k) issaved.

Using the values s_(k) provided by the previously presented algorithm,the optimum MCS, k₀, is found by using (18) and the powers allocated tothe cluster's eigemodes are computed based on (14) as follows:$\begin{matrix}{P_{j_{n}} = \left\{ \begin{matrix}{{{\overset{\sim}{P}}_{j_{n}}\left( \gamma_{k_{0}} \right)} = {\gamma_{k_{0}}\frac{N_{0}}{g_{j_{n}}}}} & {if} & {n \leq s_{k_{0}}} \\0 & {if} & {n > s_{k_{0}}}\end{matrix} \right.} & (21)\end{matrix}$

-   -   wherein    -   j_(n) means the n'th entry in the indexes vector j given by (19)    -   P_(j) _(n) means the power allocated to the j_(n)'th eigenmode        in the current cluster    -   k₀ means the index of the selected MCS in the current cluster    -   γ_(k) ₀ means the SNR required by the selected MCS (k₀) at the        current cluster to achieve the target FER in AWGN channel    -   s_(k) ₀ means the number of the selected eigenmodes in the        current cluster

If correlation between antennas is strong and/or signal-to-noise-rationis low, it is possible that the number of selected eigenmodes at theweakest cluster is small and the resulted code word is very short. Insuch a case, the performance of codes may decrease and the target FER(frame error rate) is not maintained. In this case, the weakest clusteris not used and the power pre-allocated to the weakest cluster is reusedby the strongest cluster. The arrow 224 depicts this procedure.

The method ends in block 220. The arrow 222 depicts one possibility ofrepeating the method.

Next, an example of a transmitter structure according to an embodimentof the data loading method described above is depicted in further detailby the aid of FIG. 3. It is obvious for a skilled person that thestructure of a transmitter may vary from what is depicted in FIG. 3.

The present invention can be applied in various wireless communicationsystems based on MIMO-OFDM. One example of such a communication systemis IEEE 802.11a wireless LAN communication system. In FIG. 3, there isdepicted only a part of an OFDM transmitter focusing on the transmitterblocks required by the loading method. It is obvious to a person skilledin the art that the transmitter may also include elements other thanthose illustrated in FIG. 3.

The transmitter described here is thought to be constructed of twoparts: of pre-processor 318 and of OFDM-modulator 320. The data loadingmethod described above with the aid of FIG. 2 is carried out mainly inbit and power loading block 304 which is a part of the pre-processor318.

The embodiment of the data loading method described above is based onthat the transmitter has prior knowledge of the radio channel. The radiochannel can be estimated at a receiver being coupled to the transmitteron the basis of one or more signals received in selected previoustimeslots. The link adaptation consists of modifying transmissionparameters according to channel variation in order to maximise thethroughput using at most the maximum transmission power and fulfillingrequirements set for the reliability of the transmission. Typically, thereliability is evaluated in terms of frame error rate (FER) or bit errorrate (BER).

The receiver being coupled to the transmitter is not depicted in FIG. 3for the sake of clarity. The receiver is in this embodiment a prior artMIMO-OFDM receiver.

The transmitter typically informs the receiver about the chosentransmission parameters, for instance selected eigenmodes,constellations, powers allocated to the eigenmodes and channel codeparameters, using a signalling channel.

Channel estimation block 300 carries out channel estimation for singularvalue decomposition SVD which in turn is carried out in block 302. Boththe channel estimation and SVD are described above with the aid of FIG.2.

Encoding and interleaving blocks 306A-306B carry out channel coding,interleaving and modulation. In one embodiment, channel coding iscarried out by encoding each cluster independently by using a singleinput single output (SISO) turbo code. For each cluster the encoding isperformed jointly in time and frequency domain, a codeword covering theselected eigenmodes from one cluster during whole transmitted frame. Inthis way the codeword is enlarged to achieve interleaving gain while theadaptability between clusters is still preserved. The resulted bits areusually also interleaved and modulated using some prior art interleavingand modulation methods.

In the linear pre-combining blocks 310A-310B, the complex symbolsgenerated in the blocks 306A-306B are scaled according to the powersallocated to each eigenmodes in block 304 and then the transmittedsignal at each sub-carrier is filtered with the right hand singularmatrix {circumflex over (V)}_(c) (see FIG. 2, block 204). The number ofpre-combining blocks is equal to the number of subcarriers.

In OFDM-modulator of the example of FIG. 3, an IFFT is carried out inIFFT blocks 312A-312B. It is obvious to a skilled person that the numberof blocks may vary according to the implementation. Typically, it isequal to the number of transmit antennas. Then, in blocks 314A-314B, thesignal is converted from parallel to serial form and a cyclic prefix isadded.

The OFDM-signal is then conveyed via a DAC (digital-to-analogueconverter) to the antenna 316A-316B. The number of antennas varyaccording to the implementation. In MIMO systems, there are severalantennas.

OFDM-systems are well known in the art.

Even though the invention is described above with reference to anexample according to the accompanying drawings, it is clear that theinvention is not restricted thereto but it can be modified in severalways within the scope of the appended claim.

1. A data loading method in a communication system where subcarriersinclude eigenmodes, comprising: estimating a channel matrix; calculatinga singular value decomposition of the estimated channel matrix forobtaining eigenvalue estimates; defining biases between eigenvalues andeigenvalue estimates and performing a channel estimation reliabilitytest based on the defined biases; carrying out bias compensation foreigenvalue estimates based on the defined biases; calculating equivalentpower gain; arranging eigenmodes into a predetermined number ofclusters, each cluster comprising eigenmodes of different qualitylevels; pre-allocating transmission power to the eigenmodes according totheir capacity by using the calculated equivalent power gain;determining collective transmission power to be allocated to eachcluster based on the pre-allocation; selecting the optimum modulationand coding scheme and allocating collective transmission power to theeigenmodes.
 2. The method of claim 1, wherein the number of clusters istwo and each cluster includes the strongest and the weakest eigenmodesof each subcarrier.
 3. The method of claim 1, wherein the pre-allocationof transmission power is carried out by using water filling or modifiedwater filling power allocation method.
 4. The method of claim 1, whereinthe communication system is a combined multiple-input-multiple output(MIMO) and orthogonal frequency division multiplexing (OFDM) system. 5.The method of claim 1, wherein the transmission power allocating toeigenmodes is started from the weakest cluster.
 6. The method of claim1, wherein the optimum modulation and coding scheme (MCS) whichmaximises the throughput under the total transmitted power and themaximum frame error rate (FER) constraints is found by using:$k_{0} = {{\underset{{k = 1},2,\quad\ldots\quad,K}{\arg\quad\max}{b_{k}\left( {\max\limits_{S \Subset {{{\{{1,2,\quad\ldots\quad,C}\}}\text{:}{\sum\limits_{c \in S}{{\overset{\sim}{P}}_{c}{(\gamma_{k})}}}} \leq P}}{S}} \right)}} = {\arg\quad{\underset{k}{\quad\max\quad}{b_{k}{s_{k}.}}}}}$7. The method of claim 1, wherein the power required at the eigenmode(i,c) in order to obtain a given target signal to noise ratio, SNR_(i),is given by:${{\overset{\sim}{P}}_{i,c}\left( {SNR}_{t} \right)} = {{SNR}_{t}{\frac{N_{0}}{g_{i,c}}.}}$8. The method of claim 1, wherein according to the modified waterfilling, the power to be pre-allocated for an eigenmode is expressed:$P_{i,c}^{({MWF})} = {{\min\left\lbrack {\left( {\mu - {\frac{N_{0}}{g_{i,c}}G}} \right)^{+},{\frac{N_{0}}{g_{i,c}}\gamma_{K}}} \right\rbrack}.}$9. The method of claim 1, wherein the powers allocated to the cluster'seigemodes are computed as follows: $P_{j_{n}} = \left\{ {\begin{matrix}{{{\overset{\sim}{P}}_{j_{n}}\left( \gamma_{k_{0}} \right)} = {\gamma_{k_{0}}\frac{N_{0}}{g_{j_{n}}}}} & {{{if}\quad n} \leq s_{k_{0}}} \\0 & {{{if}\quad n} > s_{k_{0}}}\end{matrix}.} \right.$
 10. The method of claim 1, wherein the clustersare encoded.
 11. A transmitter of a communication system wheresub-carriers are divided into eigenmodes, comprising: means (300) forestimating a channel matrix; means (302) for calculating a singularvalue decomposition of the estimated channel matrix for obtainingeigenvalue estimates; means (304) for defining biases betweeneigenvalues and eigenvalue estimates and performing a channel estimationreliability test based on the defined biases; means (304) for carryingout bias compensation for eigenvalue estimates based on the definedbiases; means (304) for calculating equivalent power gain; means (304)for arranging eigenmodes into a predetermined number of clusters, eachcluster comprising eigenmodes of different quality levels; means (304)for pre-allocating transmission power to the eigenmodes according totheir capacity by using the calculated equivalent power gain; means(304) for determining collective transmission power to be allocated toeach cluster based on the pre-allocation; means (304) for selecting theoptimum modulation and coding scheme and allocating collectivetransmission power to the eigenmodes.
 12. The transmitter of claim 11,further comprising means (304) for pre-allocating transmission power byusing water filling or modified water filling power allocation method.13. The transmitter of claim 11, wherein the communication system is acombined multiple-input-multiple output (MIMO) and orthogonal frequencydivision multiplexing (OFDM) system.
 14. The transmitter of claim 11,further comprising means (304) for allocating transmission power toeigenmodes starting from the weakest cluster.
 15. The transmitter ofclaim 11, further comprising means (304) for searching for themodulation and coding scheme (MCS) which maximises the throughput underthe total transmitted power and the maximum frame error rate (FER)constraints can be written as$k_{0} = {{\underset{{k = 1},2,\ldots\quad,K}{\arg\quad\max}{b_{k}\left( {\max\limits_{{S \Subset {\{{1,2,\ldots\quad,C}\}}}:{{\sum\limits_{c \in S}{{\overset{\sim}{P}}_{c}{(\gamma_{k})}}} \leq P}}{S}} \right)}} = {\arg\quad{\max\limits_{k}{b_{k}{s_{k}.}}}}}$16. The transmitter of claim 11, wherein the power required at theeigenmode (i,c) in order to obtain a given target signal to noise ratio,SNR_(i), is given by:${{\overset{\sim}{P}}_{i,c}\left( {SNR}_{t} \right)} = {{SNR}_{t}{\frac{N_{0}}{g_{i,c}}.}}$17. The transmitter of claim 11, wherein according to the modified waterfilling, the power to be pre-allocated for an eigenmode is expressed:$P_{i,c}^{({MWF})} = {{\min\left\lbrack {\left( {\mu - {\frac{N_{0}}{g_{i,c}}G}} \right)^{+},{\frac{N_{0}}{g_{i,c}}\gamma_{K}}} \right\rbrack}.}$18. The transmitter of claim 11, wherein the powers allocated to thecluster's eigemodes are computed as follows:$P_{j_{n}} = \left\{ {\begin{matrix}{{{\overset{\sim}{P}}_{j_{n}}\left( \gamma_{k_{0}} \right)} = {\gamma_{k_{0}}\frac{N_{0}}{g_{j_{n}}}}} & {{{if}\quad n} \leq s_{k_{0}}} \\0 & {{{if}\quad n} > s_{k_{0}}}\end{matrix}.} \right.$
 19. The transmitter of claim 11, furthercomprising means (306A-306B) for encoding clusters.
 20. A transmitter ofa communication system where sub-carriers are divided into eigenmodes,configured to: estimate a channel matrix; calculate a singular valuedecomposition of the estimated channel matrix for obtaining eigenvalueestimates; define biases between eigenvalues and eigenvalue estimatesand performing a channel estimation reliability test based on thedefined biases; carry out bias compensation for eigenvalue estimatesbased on the defined biases; calculate equivalent power gain; arrangeeigenmodes into a predetermined number of clusters, each clustercomprising eigenmodes of different quality levels; pre-allocatetransmission power to the eigenmodes according to their capacity byusing the calculated equivalent power gain; determine collectivetransmission power to be allocated to each cluster based on thepre-allocation; select the optimum modulation and coding scheme andallocating collective transmission power to the eigenmodes.
 21. A basestation of a communication system where sub-carriers are divided intoeigenmodes, comprising: means (300) for estimating a channel matrix;means (302) for calculating a singular value decomposition of theestimated channel matrix for obtaining eigenvalue estimates; means (304)for defining biases between eigenvalues and eigenvalue estimates andperforming a channel estimation reliability test based on the definedbiases; means (304) for carrying out bias compensation for eigenvalueestimates based on the defined biases; means (304) for calculatingequivalent power gain; means (304) for arranging eigenmodes into apredetermined number of clusters, each cluster comprising eigenmodes ofdifferent quality levels; means (304) for pre-allocating transmissionpower to the eigenmodes according to their capacity by using thecalculated equivalent power gain; means (304) for determining collectivetransmission power to be allocated to each cluster based on thepre-allocation; means (304) for selecting the optimum modulation andcoding scheme and allocating collective transmission power to theeigenmodes.
 22. A base station of a communication system, wheresub-carriers are divided into eigenmodes, configured to: estimate achannel matrix; calculate a singular value decomposition of theestimated channel matrix for obtaining eigenvalue estimates; definebiases between eigenvalues and eigenvalue estimates and performing achannel estimation reliability test based on the defined biases; carryout bias compensation for eigenvalue estimates based on the definedbiases; calculate equivalent power gain; arrange eigenmodes into apredetermined number of clusters, each cluster comprising eigenmodes ofdifferent quality levels; pre-allocate transmission power to theeigenmodes according to their capacity by using the calculatedequivalent power gain; determine collective transmission power to beallocated to each cluster based on the pre-allocation; select theoptimum modulation and coding scheme and allocating collectivetransmission power to the eigenmodes.